Update about.liquid

Author: anujkarpatne

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           The key motivation behind KGML is to improve the interpretability and generalization power of ML models, especially on out-of-sample distributions and even in the paucity of gold-standard data. 
-          To gain more insights into our research, have a look at this recent tutorial given by Anuj Karpatne at the [KGML2024 Workshop](https://sites.google.com/umn.edu/kgml2024/) on the current state and future prospects of research in KGML.
+          To gain more insights into our research, have a look at this recent tutorial given by Anuj Karpatne at the <a href="https://sites.google.com/umn.edu/kgml2024/">KGML2024 Workshop</a> on the current state and future prospects of research in KGML.
         </p>
         <iframe width="100%" height="515" src="https://www.youtube.com/embed/5bFEnWXCAfY" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe> -->
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